fix and enable pulse gradient with broadcasting on new device

doc/releases/changelog-dev.md

@@ -21,6 +21,7 @@
   accessible by the short name `default.qubit.legacy`, or directly via `qml.devices.DefaultQubitLegacy`.
   [(#4594)](https://github.com/PennyLaneAI/pennylane/pull/4594)
   [(#4436)](https://github.com/PennyLaneAI/pennylane/pull/4436)
+  [(#4620)](https://github.com/PennyLaneAI/pennylane/pull/4620)
 
 <h3>Improvements 🛠</h3>
 
@@ -94,9 +95,10 @@
   `DefaultQubitJax` in the old API.
   [(#4596)](https://github.com/PennyLaneAI/pennylane/pull/4596)
 
-* DefaultQubit2 dispatches to a faster implementation for applying `ParameterizedEvolution` to a state
+* DefaultQubit2 dispatches to a faster implementation for applying `ParametrizedEvolution` to a state
   when it is more efficient to evolve the state than the operation matrix.
   [(#4598)](https://github.com/PennyLaneAI/pennylane/pull/4598)
+  [(#4620)](https://github.com/PennyLaneAI/pennylane/pull/4620)
 
 * `ShotAdaptiveOptimizer` has been updated to pass shots to QNode executions instead of overriding
   device shots before execution. This makes it compatible with the new device API.

pennylane/devices/qubit/apply_operation.py

@@ -282,9 +282,9 @@ def apply_parametrized_evolution(
 ):
     """Apply ParametrizedEvolution by evolving the state rather than the operator matrix
     if we are operating on more than half of the subsystem"""
-    if is_state_batched:
+    if is_state_batched and op.batch_size is None:
         raise RuntimeError(
-            "ParameterizedEvolution does not support batching, but received a batched state"
+            "ParameterizedEvolution does not support standard broadcasting, but received a batched state"
         )
 
     # shape(state) is static (not a tracer), we can use an if statement

pennylane/gradients/pulse_generator_gradient.py

@@ -174,7 +174,8 @@ def _insert_op(tape, ops, op_idx):
     ops_pre = tape.operations[:op_idx]
     ops_post = tape.operations[op_idx:]
     return [
-        qml.tape.QuantumScript(ops_pre + [insert] + ops_post, tape.measurements) for insert in ops
+        qml.tape.QuantumScript(ops_pre + [insert] + ops_post, tape.measurements, shots=tape.shots)
+        for insert in ops
     ]
 
 

tests/devices/qubit/test_apply_operation.py

@@ -495,7 +495,7 @@ def test_batched_state_raises_an_error(self):
             ]
         )
 
-        with pytest.raises(RuntimeError, match="does not support batching"):
+        with pytest.raises(RuntimeError, match="does not support standard broadcasting"):
             _ = apply_operation(op, initial_state, is_state_batched=True)
 
 

tests/gradients/core/test_pulse_generator_gradient.py

@@ -965,9 +965,8 @@ def test_all_zero_diff_methods_multiple_returns_tape(self):
         assert np.allclose(res_pulse_gen[1][2], 0)
 
 
-# TODO: add default.qubit once it supports PRNG key
 @pytest.mark.jax
-@pytest.mark.parametrize("dev_name", ["default.qubit.jax"])
+@pytest.mark.parametrize("dev_name", ["default.qubit", "default.qubit.jax"])
 class TestPulseGeneratorTape:
     """Test that differentiating tapes with ``pulse_generator`` works."""
 
@@ -979,7 +978,8 @@ def test_single_pulse_single_term(self, dev_name, shots, tol):
         import jax.numpy as jnp
 
         prng_key = jax.random.PRNGKey(8251)
-        dev = qml.device(dev_name, wires=1, shots=shots, prng_key=prng_key)
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev = qml.device(dev_name, wires=1, shots=shots, **{key: prng_key})
 
         H = jnp.polyval * X(0)
         x = jnp.array([0.4, 0.2, 0.1])
@@ -1015,7 +1015,8 @@ def test_single_pulse_multi_term(self, dev_name, shots, tol):
 
         prng_key = jax.random.PRNGKey(8251)
         dev = qml.device(dev_name, wires=1, shots=None)
-        dev_shots = qml.device(dev_name, wires=1, shots=shots, prng_key=prng_key)
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev_shots = qml.device(dev_name, wires=1, shots=shots, **{key: prng_key})
 
         H = 0.1 * Z(0) + jnp.polyval * X(0) + qml.pulse.constant * Y(0)
         x = jnp.array([0.4, 0.2, 0.1])
@@ -1095,7 +1096,8 @@ def test_multi_pulse(self, dev_name, shots, tol):
 
         prng_key = jax.random.PRNGKey(8251)
         dev = qml.device(dev_name, wires=1, shots=None)
-        dev_shots = qml.device(dev_name, wires=1, shots=shots, prng_key=prng_key)
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev_shots = qml.device(dev_name, wires=1, shots=shots, **{key: prng_key})
 
         H0 = 0.1 * Z(0) + jnp.polyval * X(0)
         H1 = 0.2 * Y(0) + qml.pulse.constant * Y(0) + jnp.polyval * Z(0)
@@ -1447,9 +1449,8 @@ def circuit(param1, param2):
             assert tracker.totals["executions"] == 1 + 2  # one forward pass, two shifted tapes
 
 
-# TODO: port ParametrizedEvolution to new default.qubit
 @pytest.mark.jax
-@pytest.mark.parametrize("dev_name", ["default.qubit.jax"])
+@pytest.mark.parametrize("dev_name", ["default.qubit", "default.qubit.jax"])
 class TestPulseGeneratorDiff:
     """Test that pulse_generator is differentiable, i.e. that computing
     the derivative with pulse_generator is differentiable a second time,
@@ -1473,7 +1474,7 @@ def fun(params):
             tape = qml.tape.QuantumScript([op], [qml.expval(Z(0))])
             tape.trainable_params = [0]
             _tapes, fn = pulse_generator(tape)
-            return fn(qml.execute(_tapes, dev, None))
+            return fn(qml.execute(_tapes, dev, "backprop"))
 
         params = [jnp.array(0.4)]
         p = params[0] * T

tests/gradients/core/test_pulse_gradient.py

@@ -1352,9 +1352,8 @@ def circuit(params):
         assert tracker.totals["executions"] == 4  # two shifted tapes, two splitting times
 
 
-# TODO: add default.qubit once it supports PRNG key
 @pytest.mark.jax
-@pytest.mark.parametrize("dev_name", ["default.qubit.jax"])
+@pytest.mark.parametrize("dev_name", ["default.qubit", "default.qubit.jax"])
 class TestStochPulseGradIntegration:
     """Test that stoch_pulse_grad integrates correctly with QNodes and ML interfaces."""
 
@@ -1367,7 +1366,8 @@ def test_simple_qnode_expval(self, dev_name, num_split_times, shots, tol):
         import jax.numpy as jnp
 
         jax.config.update("jax_enable_x64", True)
-        dev = qml.device(dev_name, wires=1, shots=shots, prng_key=jax.random.PRNGKey(74))
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev = qml.device(dev_name, wires=1, shots=shots, **{key: jax.random.PRNGKey(74)})
         T = 0.2
         ham_single_q_const = qml.pulse.constant * qml.PauliY(0)
 
@@ -1394,7 +1394,8 @@ def test_simple_qnode_expval_two_evolves(self, dev_name, num_split_times, shots,
         import jax.numpy as jnp
 
         jax.config.update("jax_enable_x64", True)
-        dev = qml.device(dev_name, wires=1, shots=shots, prng_key=jax.random.PRNGKey(74))
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev = qml.device(dev_name, wires=1, shots=shots, **{key: jax.random.PRNGKey(74)})
         T_x = 0.1
         T_y = 0.2
         ham_x = qml.pulse.constant * qml.PauliX(0)
@@ -1425,7 +1426,8 @@ def test_simple_qnode_probs(self, dev_name, num_split_times, shots, tol):
         import jax.numpy as jnp
 
         jax.config.update("jax_enable_x64", True)
-        dev = qml.device(dev_name, wires=1, shots=shots, prng_key=jax.random.PRNGKey(74))
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev = qml.device(dev_name, wires=1, shots=shots, **{key: jax.random.PRNGKey(74)})
         T = 0.2
         ham_single_q_const = qml.pulse.constant * qml.PauliY(0)
 
@@ -1452,7 +1454,8 @@ def test_simple_qnode_probs_expval(self, dev_name, num_split_times, shots, tol):
         import jax.numpy as jnp
 
         jax.config.update("jax_enable_x64", True)
-        dev = qml.device(dev_name, wires=1, shots=shots, prng_key=jax.random.PRNGKey(74))
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev = qml.device(dev_name, wires=1, shots=shots, **{key: jax.random.PRNGKey(74)})
         T = 0.2
         ham_single_q_const = qml.pulse.constant * qml.PauliY(0)
 
@@ -1535,8 +1538,13 @@ def ansatz(params):
         )
         qnode_backprop = qml.QNode(ansatz, dev, interface="jax")
 
-        grad_pulse_grad = jax.grad(qnode_pulse_grad)(params)
-        assert dev.num_executions == 1 + 2 * 3 * num_split_times
+        with qml.Tracker(dev) as tracker:
+            grad_pulse_grad = jax.grad(qnode_pulse_grad)(params)
+        assert (
+            tracker.totals["executions"] == (1 + 2 * 3 * num_split_times)
+            if dev_name == "default.qubit.jax"
+            else 1
+        )
         grad_backprop = jax.grad(qnode_backprop)(params)
 
         assert all(
@@ -1552,7 +1560,8 @@ def test_multi_return_broadcasting_multi_shots_raises(self, dev_name):
 
         jax.config.update("jax_enable_x64", True)
         shots = [100, 100]
-        dev = qml.device(dev_name, wires=1, shots=shots, prng_key=jax.random.PRNGKey(74))
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev = qml.device(dev_name, wires=1, shots=shots, **{key: jax.random.PRNGKey(74)})
         T = 0.2
         ham_single_q_const = qml.pulse.constant * qml.PauliY(0)
 
@@ -1582,7 +1591,8 @@ def test_qnode_probs_expval_broadcasting(self, dev_name, num_split_times, shots,
         import jax.numpy as jnp
 
         jax.config.update("jax_enable_x64", True)
-        dev = qml.device(dev_name, wires=1, shots=shots, prng_key=jax.random.PRNGKey(74))
+        key = "prng_key" if dev_name == "default.qubit.jax" else "seed"
+        dev = qml.device(dev_name, wires=1, shots=shots, **{key: jax.random.PRNGKey(74)})
         T = 0.2
         ham_single_q_const = qml.pulse.constant * qml.PauliY(0)
 
@@ -1757,9 +1767,8 @@ def ansatz(params):
         jax.clear_caches()
 
 
-# TODO: port ParametrizedEvolution to new default.qubit
 @pytest.mark.jax
-@pytest.mark.parametrize("dev_name", ["default.qubit.jax"])
+@pytest.mark.parametrize("dev_name", ["default.qubit", "default.qubit.jax"])
 class TestStochPulseGradDiff:
     """Test that stoch_pulse_grad is differentiable."""
 
@@ -1780,7 +1789,7 @@ def fun(params):
             tape = qml.tape.QuantumScript([op], [qml.expval(qml.PauliZ(0))])
             tape.trainable_params = [0]
             tapes, fn = stoch_pulse_grad(tape)
-            return fn(qml.execute(tapes, dev, None))
+            return fn(qml.execute(tapes, dev, "backprop"))
 
         params = [jnp.array(0.4)]
         p = params[0] * T